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Record W4384911742 · doi:10.3138/ijcs-2021-0009

Climate Change Attitudes and Fossil Fuel Extraction and Distribution in Canada

2023· article· en· W4384911742 on OpenAlexaffvenueabout
Lisa Y. Seiler, Glenn J. Stalker

Bibliographic record

VenueInternational Journal of Canadian Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsYork University
Fundersnot available
KeywordsClimate changeOpposition (politics)Framing (construction)Biology and political orientationPoliticsArcticPublic opinionFossil fuelPolitical scienceNatural resource economicsPolitical economyEconomicsGeographyEngineeringEcology

Abstract

fetched live from OpenAlex

It is widely accepted that the burning of fossil fuels is a major contributor to climate change. Our question is: How are the related extraction and distribution activities viewed in Canada? This article analyzes Canadian public opinion data on five supply-side energy policies: expanding the oil sands, drilling for oil in the Arctic, fracking, expanding an oil pipeline, and shipping oil by rail. It applies social psychological models to identify factors associated with support for and opposition to these policies. Climate change attitudes have typically been found to be significant predictors of climate policy support. Instead, this study finds that having an ecological worldview is a strong predictor for each of the policies. This suggests that these policies are seen as having an effect on the environment but less so as affecting climate change. Contextual factors, such as region of residence and political orientation, are relevant predictors, suggesting that framing by political parties, industry, and social movement organizations has had a significant effect on support and opposition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.471
GPT teacher head0.480
Teacher spread0.009 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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